Genesis of interictal spikes in the CA1: a computational investigation

Abstract

Interictal spikes (IISs) are spontaneous high amplitude, short time duration <400 ms events often observed in electroencephalographs (EEG) of epileptic patients. In vitro analysis of resected mesial temporal lobe tissue from patients with refractory temporal lobe epilepsy has revealed the presence of IIS in the CA1 subfield. In this paper, we develop a biophysically relevant network model of the CA1 subfield and investigate how changes in the network properties influence the susceptibility of CA1 to exhibit an IIS. We present a novel template based approach to identify conditions under which synchronization of paroxysmal depolarization shift (PDS) events evoked in CA1 pyramidal (Py) cells can trigger an IIS. The results from this analysis are used to identify the synaptic parameters of a minimal network model that is capable of generating PDS in response to afferent synaptic input. The minimal network model parameters are then incorporated into a detailed network model of the CA1 subfield in order to address the following questions: (1) How does the formation of an IIS in the CA1 depend on the degree of sprouting (recurrent connections) between the CA1 Py cells and the fraction of CA3 Shaffer collateral (SC) connections onto the CA1 Py cells? and (2) Is synchronous afferent input from the SC essential for the CA1 to exhibit IIS? Our results suggest that the CA1 subfield with low recurrent connectivity (absence of sprouting), mimicking the topology of a normal brain, has a very low probability of producing an IIS except when a large fraction of CA1 neurons (>80%) receives a barrage of quasi-synchronous afferent input (input occurring within a temporal window of ≤24 ms) via the SC. However, as we increase the recurrent connectivity of the CA1 (Psprout > 40); mimicking sprouting in a pathological CA1 network, the CA1 can exhibit IIS even in the absence of a barrage of quasi-synchronous afferents from the SC (input occurring within temporal window >80 ms) and a low fraction of CA1 Py cells (≈30%) receiving SC input. Furthermore, we find that in the presence of Poisson distributed random input via SC, the CA1 network is able to generate spontaneous periodic IISs (≈3 Hz) for high degrees of recurrent Py connectivity (Psprout > 70). We investigate the conditions necessary for this phenomenon and find that spontaneous IISs closely depend on the degree of the network's intrinsic excitability.

Computational models of epileptiform activity

“…Another detailed network model of interictal epileptic spikes in CA1 has been recently published (Ratnadurai-Giridharan et al, 2014). Also based on the Hodgkin-Huxley formalism, glutamater-Q6 gic pyramidal cells were modeled according to (Golomb et al, 2006), basket cells were modeled from (Wang and Buzsaki, 1996) and oriens-alveus (OA) cells were taken from (Wang, 2002).…”

Adaptation and inhibition control pathological synchronization in a model of focal epileptic seizure

“…The alternative potential mechanism of termination of these bursts are GABAB currents provided by the inhibitory population (de la Prida et al 2006). We predict that in the complete absence of the inhibitory neurotransmission including GABAA and GABAB synapses, the purely excitatory network in the epileptogenic slice of human subiculum would be capable of generating self-sustained pre-ictal oscillations due to negative feedback provided by AHP (Ratnadurai-Giridharan et al, 2014) and other intrinsic adaptation currents. Therefore the downregulation of excitatory neuronal adaptation currents such as AHP and/or functionally similar muscarinic-sensitive potassium currents (Stiefel et al, 2008) could lead to seizure initiation.…”

“…A common problem with neural mass models in general is their limited ability to generate the experimentally measurable signals (Lytton, 2009). In this work we used the average voltage of the excitatory neural population as the approximation of the LFP signal near the neurons' somas (Ratnadurai-Giridharan et al, 2014). Given the distant-dependence of the LFP signal, this model should be considered only as a first approximation (Buzsáki et al, 2012).…”

“…Thus, to approximate the LFP being recorded near somas of the excitatory populations, we used the assumption that the average membrane potential of the excitatory population is proportional to the LFP, i.e. (Ursino et al, 2006;Demont-Guignard et al, 2009;Wendling et al, 2012;Ratnadurai-Giridharan et al, 2014).…”

“…1A, computed according to the population firing-rates . Similar to spiking neural network models (Bazhenov et al, 2004), (Ratnadurai-Giridharan et al, 2014), adaption in our population model reduces neural firing in the excitatory population after periods of activity. Excitatory population receives external random synaptic input to model excitation from the rest of the brain similar to (Touboul et al, 2011;Jansen et al, 1995).…”